no code implementations • 15 Mar 2023 • Inhwa Han, Serin Yang, Taesung Kwon, Jong Chul Ye
Diffusion models have shown superior performance in image generation and manipulation, but the inherent stochasticity presents challenges in preserving and manipulating image content and identity.
no code implementations • CVPR 2022 • Kwanyoung Kim, Taesung Kwon, Jong Chul Ye
Through extensive experiments, we demonstrate that the proposed method can accurately estimate noise models and parameters, and provide the state-of-the-art self-supervised image denoising performance in the benchmark dataset and real-world dataset.
1 code implementation • CVPR 2022 • Gwanghyun Kim, Taesung Kwon, Jong Chul Ye
To mitigate these problems and enable faithful manipulation of real images, we propose a novel method, dubbed DiffusionCLIP, that performs text-driven image manipulation using diffusion models.
no code implementations • 17 Apr 2021 • Taesung Kwon, Jong Chul Ye
Recently, CycleGAN was shown to provide high-performance, ultra-fast denoising for low-dose X-ray computed tomography (CT) without the need for a paired training dataset.